Calculation Of Curves Research Articles

Dissociative photoionization of acetaldehyde in the 10.2-19.5 eV VUV range.

The valence-shell dissociative photoionization of acetaldehyde has been investigated by means of the photoion photoelectron coincidence technique in conjunction with tuneable synchrotron radiation. The experimental results consist of threshold photoelectron spectra for the parent ion and for each fragment ion in the 10.2-19.5 eV photon energy range, along with (ion, e) kinetic energy coincidence diagrams obtained from measurements at fixed photon energies. The results are complemented by high-level ab initio calculations of potential energy curves as a function of the C-H bond distance. The nudged elastic band (NEB) method has been employed to connect the parent ion Franck-Condon region to the formation of the HCO+, CH3+ and CH4+ ion fragments. Appearance energies have been determined for six fragment ions with an improved accuracy, including two fragmentation channels, which to the best of our knowledge have not been reported previously, i.e. the formation of CH2CO+, lying at 13.10 ± 0.05 eV, and the formation of CH2+ at 15.1 ± 0.1 eV. Based on both experimental and theoretical results, the dissociation dynamics following ionization of acetaldehyde into the different fragmentation channels are discussed.

Regioselective and solvent-dependent photoisomerization induced internal conversion in red fluorescent protein chromophore analogues.

Photophysical properties of three red fluorescent protein (RFP) chromophore analogues are reported here. The three RFP chromophore analogues differ in the additional conjugation present in the RFP chromophore. The three chromophores do not exhibit any solvent effect in both absorption and fluorescence spectra. The photoirradiation experiments and recording of 1H NMR before and after irradiation on one of the three RFP model chromophores show isomerization of the (Z,E) diastereomer to the (E,E) diastereomer. Calculation of S0 and S1 potential energy curves shows the preference for isomerization through the exocyclic C[double bond, length as m-dash]C bond with Z-stereochemistry, thus corroborating the experimental results. The computational studies also suggest that torsional motion along the exocyclic C[double bond, length as m-dash]C bond pushes the molecules to a conical intersection, thus paving the pathway for radiationless deactivation.

Calculation of the total number of radiation decay of Radiolabelled-Octreotate using non-linear mixed effect models

Non-Linear Mixed Effect (NLME) is a method used in the area under the measured time-activity curve (AUC) calculations. The calculation of an accurate AUC is needed for an accurate determination of the radiation absorbed dose. In NLME, the error model might affect the accuracy of the estimation of the AUC. Therefore, the aim of this study was to determine the effect of error models on AUC calculations using NMLE. The data used in this study were from biokinetic data of the 111In-DOTATATE biodistribution in the tumour collected from the literature. The data were fitted using published bi-exponential function $f(t) = {{({{\rm{k}}_e} \times {{\rm{k}}_a})} \over {c({{\rm{k}}_a} - {{\rm{k}}_e})}}\left[ {{e^{ - ({{\rm{k}}_e})t}} - {e^{ - ({{\rm{k}}_a})t}}} \right]$ with several error models, namely constant, proportional, combined and exponential errors. The mean and standard deviation were determined from the AUC for each error model AUC values obtained from constant, proportional, combined, and exponential error were (4.40 ± 1.93) nmol·min, (3.13 ± 2.74) nmol·min, (3.22 ± 2.85) nmol·min and (3.14 ± 2.75) nmol·min, respectively. Based on the research results, the proportional, combined and exponential error were relatively produced better results compared to the constant error model in our dataset.

The Use of Corticosteroid Randomisation after Significant Head Injury (CRASH) Prognostic Model as Mortality Predictor of Traumatic Brain Injury Patients Underwent Surgery in Low-Middle Income Countries.

Traumatic brain injury (TBI) is a disruption to normal brain functions caused by traumas such as collisions, blows, or penetrating injuries. There are factors affecting patient outcomes that also have a predictive value. Limited data from low-middle income countries showed a high number of poor outcomes in TBI patients. The corticosteroid randomisation after significant head injury (CRASH) prognostic model is a predictive model that uses such factors and is often used in developed countries. The model has an excellent discriminative ability. However, there is still a lack of studies on its use in surgical patients in low-middle income countries. This study aimed to evaluate the CRASH model's validity to predict 14-day mortality of TBI patients who underwent surgery in low-middle income countries. This retrospective analytical observational study employed total sampling including all TBI patients who underwent surgery with general anesthesia from January to December 2022. Statistical analysis was performed by applying Mann-Whitney and Fisher exact tests, while the model's discriminative ability was determined through the area under the curve (AUC) calculations. 112 TBI patients were admitted during the study period, and 74 patients were included. Independent statistical analysis showed that 14-day mortality risk, age, Glasgow Coma Scale score, TBI severity, pupillary response, and major extracranial trauma had a significant individual correlation with patients' actual mortality outcome (p < 0.05). The AUC analysis revealed an excellent mortality prediction (AUC 0.838; CI 95%). The CRASH prognostic model performs well in predicting the 14-day mortality of TBI patients who underwent surgery in low-middle income countries.

Effects of thickness and roughness on plasmonic characteristics of gold thin films deposited on polished optical fiber

The thickness and roughness of metal layers substantially affect the performance of surface plasmon resonance (SPR)-based sensors. The deposition methods, control parameters, and substrate characteristics influence the layer thickness and roughness. This study investigates the SPR characteristics of a polished optical fiber surface coated with gold (Au) metal of different thicknesses. The Au layer is deposited via the thermal evaporation method, and its thickness is varied by controlling the deposition time (3–6 min). A proportionality relationship between thickness and deposition time is observed. Island-shaped structures in gold (Au) morphology are formed due to low adhesion to the substrate. The shape of this island creates gaps in the layer, causing scattering. In addition, the roughness on the gold surface triggers the Localized Surface Plasmon Resonance (LSPR) phenomenon. As a result, the measured dielectric characteristics differ from the reference. The SPR curve calculation simulation was carried out based on reference optical parameters and measurement results by an ellipsometer, which were then compared with experiments. The obtained results show that the substrate roughness, morphology, and thickness of the Au layer play an essential role in determining the characteristics of the SPR curve in a fiber optic plasmonic sensor. As a result, in basic experiments, the sample with an Au thickness of 27.37 nm (deposition time = 3 min) shows better characteristics (half-maximum full width, minimum transmittance, and resonance wavelength) compared with the sample with an Au thickness of 53.97 nm (deposition time = 4 min), Although 53.97 nm is the optimal thickness from the simulation using reference optical parameters (smooth substrate surface and smooth gold layer).

A microRNA diagnostic biomarker for amyotrophic lateral sclerosis.

Blood-based diagnostic biomarkers for amyotrophic lateral sclerosis will improve patient outcomes and positively impact novel drug development. Critical to the development of such biomarkers is robust method validation, optimization and replication with adequate sample sizes and neurological disease comparative blood samples. We sought to test an amyotrophic lateral sclerosis biomarker derived from diverse samples to determine if it is disease specific. Extracellular vesicles were extracted from blood plasma obtained from individuals diagnosed with amyotrophic lateral sclerosis, primary lateral sclerosis, Parkinson's disease and healthy controls. Immunoaffinity purification was used to create a neural-enriched extracellular vesicle fraction. MicroRNAs were measured across sample cohorts using real-time polymerase chain reaction. A Kruskal-Wallis test was used to assess differences in plasma microRNAs followed by post hoc Mann-Whitney tests to compare disease groups. Diagnostic accuracy was determined using a machine learning algorithm and a logistic regression model. We identified an eight-microRNA diagnostic signature for blood samples from amyotrophic lateral sclerosis patients with high sensitivity and specificity and an area under the curve calculation of 98% with clear statistical separation from neurological controls. The eight identified microRNAs represent disease-related biological processes consistent with amyotrophic lateral sclerosis. The direction and magnitude of gene fold regulation are consistent across four separate patient cohorts with real-time polymerase chain reaction analyses conducted in two laboratories from diverse samples and sample collection procedures. We propose that this diagnostic signature could be an aid to neurologists to supplement current clinical metrics used to diagnose amyotrophic lateral sclerosis.

Factors Influencing Sexual Experiences in Adolescents Using a Random Forest Model: Secondary Data Analysis of the 2019~2021 Korea Youth Risk Behavior Web-based Survey Data

The objective of this study was to develop a predictive model for the sexual experiences of adolescents using the random forest method and to identify the "variable importance." The study utilized data from the 2019 to 2021 Korea Youth Risk Behavior Web-based Survey, which included 86,595 man and 80,504 woman participants. The number of independent variables stood at 44. SPSS was used to conduct Rao-Scott χ² tests and complex sample t-tests. Modeling was performed using the random forest algorithm in Python. Performance evaluation of each model included assessments of precision, recall, F1-score, receiver operating characteristics curve, and area under the curve calculations derived from the confusion matrix. The prevalence of sexual experiences initially decreased during the COVID-19 pandemic, but later increased. "Variable importance" for predicting sexual experiences, ranked in the top six, included week and weekday sedentary time and internet usage time, followed by ease of cigarette purchase, age at first alcohol consumption, smoking initiation, breakfast consumption, and difficulty purchasing alcohol. Education and support programs for promoting adolescent sexual health, based on the top-ranking important variables, should be integrated with health behavior intervention programs addressing internet usage, smoking, and alcohol consumption. We recommend active utilization of the random forest analysis method to develop high-performance predictive models for effective disease prevention, treatment, and nursing care.

High-density analysis of surface wave profile imaging based on a multiple coverage common-midpoint signal-couple array

Surface wave exploration technology has been extensively used in the inspection of construction engineering quality and shallow surface surveys. To enhance the efficiency of surface wave exploration field acquisition and achieve high-precision and high-density surface wave profile imaging, a wireless distributed seismic surface wave signal acquisition system has been developed based on the principles of active source transient surface wave signal acquisition and dispersion curve calculation methods. For the purpose of achieving rapid multiple coverage signal acquisition and enhancing fieldwork efficiency, a method for rapidly configuring common-midpoint signal couples (CMCs) for multiple coverage common-shot signal acquisition has been devised, and a high-precision visualization method for dispersion curve calculation based on the CMC array has been formulated. When compared with the multichannel analysis of the surface wave method under identical conditions, the CMC array can effectively enhance surface wave dispersion curve survey station density and lateral resolution, thereby enabling high-density analysis of surface wave profile imaging. Through model analysis and field examples related to construction quality detection, including foundation compactness and earth and rock mixture compactness, it has been demonstrated that this method offers significant advantages in terms of high accuracy, high density, and a wide application range. These advantages greatly enhance the efficiency of surface wave exploration and the accuracy of profile imaging for the construction of engineering projects.

Proposal and application of onshore lattice wind turbine support structure and integrated multi-scale fatigue assessment method

To accommodate the development of wind turbines in areas with weak wind speeds, an upright ultra-high lattice support structure is proposed for the large wind turbine, which is prefabricated and assembled mainly from thin-walled circular hollow steel (CHS) tube elements. As a practical example, a 180 m wind turbine support structure is presented. To evaluate the fatigue performance of the new structure type with modular assembly, a new integrated multi-scale fatigue assessment method is proposed to address the shortcomings of the equivalent fatigue load method commonly used in engineering design. For any fatigue load condition, the integrated multi-scale fatigue assessment method enables accurate and efficient calculation of the relationship curve between fatigue load and hot spot stress, and identification of the locations in the structure where fatigue accumulation damage is the greatest. Furthermore, fatigue damage accumulation and fatigue life can also be predicted accurately and efficiently by the proposed method. The results show that for the maximum fatigue cumulative damage of the proposed new structural systems, as the effect of average stress is not considered and the assumption of superposition of principal stresses, the traditional equivalent fatigue load method is too conservative compared to the proposed integrated multi-scale fatigue assessment method, detrimental to cost decreasing and benefit increasing of the wind turbine structures.

The value of T2-weighted MRI contrast ratio combined with DWI in evaluating the pathological grade of solid lung adenocarcinoma

To assess the predictive value of T2-weighted (T2W) magnetic resonance imaging (MRI) in combination with diffusion-weighted imaging (DWI) for determining the pathological grading of solid lung adenocarcinoma. The clinical and imaging data from 153 cases of solid lung adenocarcinoma (82 men, 71 women, mean age 63.2 years) confirmed at histopathology in The First Affiliated Hospital of Xi'an Jiaotong University from January 2017 to May 2022 were analysed retrospectively. Adenocarcinomas were classified into low-grade (G1 and G2) and high-grade (G3) groups following the 2020 pathological grading system proposed by the International Association for the Study of Lung Cancer. The T2-weighted contrast ratio (T2CR), calculated as the T2 signal intensity of the lung mass/nodule divided by the T2 signal intensity of the right rhomboid muscle was utilised. Two experienced radiologists reviewed the MRI images independently, measured the T2CR, and obtained apparent diffusion coefficient (ADC) values. The Mann-Whitney U-test was used to compare general characteristics (sex, age, maximum diameter), T2CR, and ADC values between the low-grade and high-grade groups. The non-parametric Kruskal-Wallis test determined differences in T2CR and ADC values among the five adenocarcinoma subtypes. Receiver characteristic curve (ROC) analysis, along with area under the curve (AUC) calculation, assessed the effectiveness of each parameter in distinguishing the pathological grade of lung adenocarcinoma. A Z-test was used to compare the AUC values. Among the 153 patients with adenocarcinoma, 103 had low-grade adenocarcinoma, and 50 had high-grade adenocarcinoma. The agreement between T2CR and ADC observers was good (0.948 and 0.929, respectively). None of the parameters followed a normal distribution (p<0.05). The ADC value was lower in the high-grade adenocarcinoma group compared to the low-grade adenocarcinoma group (p=0.004), while the T2CR value was higher in the high-grade group (p=0.011). Statistically significant differences were observed in maximum diameter and gender between the two groups (p<0.001 and p=0.005, respectively), while no significant differences were noted in age (p=0.980). Among the five adenocarcinoma subtypes, only the lepidic and micropapillary subtypes displayed statistical differences in ADC values (p=0.047), with the remaining subtypes showing no statistical differences (p>0.05). The AUC values for distinguishing high-grade adenocarcinoma from low-grade adenocarcinoma were 0.645 for ADC and 0.627 for T2CR. Combining T2CR, ADC, sex, and maximum diameter resulted in an AUC of 0.778, sensitivity of 70%, and specificity of 75%. This combination significantly improved diagnostic efficiency compared to T2CR and ADC alone (p=0.008, z=2.624; p=0.007, z=2.679). The MRI quantitative parameters are useful for distinguishing the pathological grades of solid lung adenocarcinoma, offering valuable insights for precise lung cancer treatment.

Calculation of the Rocking Curve of a Double-Crystal Neutron Spectrometer on Strongly Absorbing InSb Crystals in Laue–Laue Geometry

Calculation of the Rocking Curve of a Double-Crystal Neutron Spectrometer on Strongly Absorbing InSb Crystals in Laue–Laue Geometry

Six‐Component Earthquake Synchronous Observations Across Taiwan Strait: Phase Velocity and Source Location

AbstractWith the development of rotational seismometers, especially the high‐sensitivity optical seismometers, rotational motions have shown increasing benefits for calculation of dispersion curves, estimation of back azimuth and many other applications. Here we present the six‐component observations of 2019 Hualien mww6.1 earthquake simultaneously on both sides of Taiwan Strait. Seismograms and time‐frequency spectrums show that the attenuations for rotations are more significant than translations. Calculations of the surface wave phase velocity are performed for both stations through single‐station records, indicating a distinct difference owning to the instrument installation conditions and site effect. Comparison of theoretical dispersion curves is consistently in trends with estimations, and reveals the influence of higher‐mode surface waves. For the first time, we locate the source of a earthquake through intersection of two stations' back azimuths estimated through rotational methods. Results of comparing to translational polarization verify that rotational observation can distinctly reduce the deviation of source location.

Individualized discrimination of tumor progression from treatment-related changes in different types of adult-type diffuse gliomas using [11C]methionine PET.

This study aimed to assess the ability of [11C]methionine (MET) PET in distinguishing between tumor progression (TP) and treatment-related changes (TRCs) among different types of adult-type diffuse gliomas according to the 2021 World Health Organization classification and predict overall survival (OS). We retrospectively selected 113 patients with adult-type diffuse gliomas with suspected TP who underwent MET PET imaging. Maximum and mean tumor-to-background ratios (TBRmax, TBRmean) and metabolic tumor volume (MTV) were calculated. Diagnoses were verified by histopathology (n = 50) or by clinical/radiological follow-up (n = 63). The diagnostic performance of MET PET parameters was evaluated through receiver operating characteristic (ROC) analysis and area under the curve (AUC) calculation. Survival analysis employed the Kaplan-Meier method and Cox proportional-hazards regression. TP and TRCs were diagnosed in 76 (67%) and 37 (33%) patients, respectively. ROC analysis revealed TBRmax had the best performance in differentiating TP from TRCs with a cut-off of 1.96 in IDH-mutant astrocytoma (AUC, 0.87; sensitivity, 93%; specificity 69%), 1.80 in IDH-mutant and 1p/19q-codeleted oligodendroglioma (AUC, 0.96; sensitivity, 100%; specificity, 89%), and 2.13 in IDH wild-type glioblastoma (AUC, 0.89; sensitivity, 89%; specificity, 78%), respectively. On multivariate analysis, higher TBRmean and MTV were significantly correlated with shorter OS in all IDH-mutant gliomas, as well as in IDH-mutant astrocytoma subgroup. This work confirms that MET PET has varying diagnostic performances in distinguishing TP from TRCs within three types of adult-type diffuse gliomas, and highlights its high diagnostic accuracy in IDH-mutant and 1p/19q-codeleted oligodendroglioma and potential prognostic value for IDH-mutant gliomas, particularly IDH-mutant astrocytoma.

Sex differences in the accuracy of vertebral bone quality score assessing bone density in patients undergoing lumbar spinal fusion.

Low bone mineral density (BMD) significantly increases the risk of complications in patients undergoing spinal fusion. Existing evidence indicates that traditional dual-energy x-ray absorptiometry (DEXA) and quantitative CT (QCT) screening are underutilized in spine surgery. The MRI-based vertebral bone quality (VBQ) score provides a tool for primary screening of bone density. The validity of this score as a predictor across sexes has not been investigated. This study aimed to explore the effect of sex on the diagnostic efficacy of the VBQ in predicting osteopenia/osteoporosis and whether a sex-specific threshold exists. In this retrospective cohort study, patients who underwent lumbar fusion at a tertiary care center were reviewed. VBQ was obtained by noncontrast T1-weighted MRI. Patients were stratified according to sex and bone density. Data were analyzed between the groups. Pearson correlation analysis and linear regression were used to analyze the correlation between the VBQ and DEXA T values. Receiver operating characteristic (ROC) curve analysis, including area under the curve (AUC) calculation, was used to evaluate the predictive performance of VBQ for low BMD in both sexes. A total of 271 patients (92 male, 179 female patients) were analyzed. The correlation coefficient between VBQ and the lowest T value was -0.40 for male and -0.554 for female patients. In comparing the bone density subgroups, among male patients a significant difference in the VBQ scores was observed only between the normal and osteoporosis subgroups (p = 0.012). VBQ demonstrated statistically significant differences among female patients across all three subgroups (p < 0.001). The ROC analysis revealed that the predictive performance of VBQ in detecting low BMD was more consistent with the gold-standard DEXA results in female than in male patients (AUC 0.647 vs AUC 0.823, p = 0.02). The optimal thresholds were similar in both sexes. Compared with male patients, VBQ has better discrimination between female patients with low BMD and those with normal bone density. Although the correlation between VBQ and bone density is weaker in male than in female patients, the optimal thresholds are similar in both sexes.

Calculation and Comparison of Adsorption Curves of Three ISO-Variables A CSE Study of Lean Coal from China

A series of isothermal adsorption experimental data of MZQ coal sample in Shanxi province of China has been transformed to fit a temperature pressure-adsorption equation with high accuracy. Besides isothermal adsorption curve, isobaric adsorption curve and isosteric adsorption curve have been created and presented. The Clausius-Clapeyron equations have been used to illustrate that gas isosteric adsorption is an exothermic process. The calculation procedure of unit isosteric adsorption enthalpy has been present. The increase of adsorption amount causes the unit isosteric adsorption enthalpy exponentially decreased, so the unit isosteric desorption enthalpy exponentially decreased.

Experimental and numerical investigation on dynamic characteristic of nozzle check valve and fluid-valve transient interaction

Check valves play an important role in multiple systems within nuclear power plants. This study focuses on the valve closing process and investigates the dynamic characteristics, valve disc motion, and flow field of a nozzle check valve under different spring stiffness and fluid deceleration rates using experimental and numerical simulation methods. Experimental tests were conducted on a DN300 annular disc-type nozzle check valve with a spring stiffness of 3000 N/m, and the dynamic characteristic curve of the check valve was obtained. The curve exhibits a small maximum slope (0.0454), indicating good dynamic performance of the check valve. Numerical simulations were performed using the dynamic mesh method and RNG k-ε turbulence model to study the effects of different spring stiffness and fluid deceleration rates on the maximum reverse flow velocity, disc motion process, disc impact velocity, and internal flow field of the valve. The maximum reverse flow velocity was also corrected, and the resulting dynamic characteristic curve agrees well with experimental values. This study analyzes the influence of spring stiffness and fluid deceleration rate on various parameters during the dynamic closing process of the check valve. This study contributes to the optimization design of check valves and provides guidance for more accurate calculation of the dynamic characteristic curve of check valves through numerical simulations.

MRI-based automated multitask deep learning system to evaluate supraspinatus tendon injuries.

To establish an automated, multitask, MRI-based deep learning system for the detailed evaluation of supraspinatus tendon (SST) injuries. According to arthroscopy findings, 3087 patients were divided into normal, degenerative, and tear groups (groups 0-2). Group 2 was further divided into bursal-side, articular-side, intratendinous, and full-thickness tear groups (groups 2.1-2.4), and external validation was performed with 573 patients. Visual geometry group network 16 (VGG16) was used for preliminary image screening. Then, the rotator cuff multitask learning (RC-MTL) model performed multitask classification (classifiers 1-4). A multistage decision model produced the final output. Model performance was evaluated by receiver operating characteristic (ROC) curve analysis and calculation of related parameters. McNemar's test was used to compare the differences in the diagnostic effects between radiologists and the model. The intraclass correlation coefficient (ICC) was used to assess the radiologists' reliability. p < 0.05 indicated statistical significance. In the in-group dataset, the area under the ROC curve (AUC) of VGG16 was 0.92, and the average AUCs of RC-MTL classifiers 1-4 were 0.99, 0.98, 0.97, and 0.97, respectively. The average AUC of the automated multitask deep learning system for groups 0-2.4 was 0.98 and 0.97 in the in-group and out-group datasets, respectively. The ICCs of the radiologists were 0.97-0.99. The automated multitask deep learning system outperformed the radiologists in classifying groups 0-2.4 in both the in-group and out-group datasets (p < 0.001). The MRI-based automated multitask deep learning system performed well in diagnosing SST injuries and is comparable to experienced radiologists. Our study established an automated multitask deep learning system to evaluate supraspinatus tendon (SST) injuries and further determine the location of SST tears. The model can potentially improve radiologists' diagnostic efficiency, reduce diagnostic variability, and accurately assess SST injuries. • A detailed classification of supraspinatus tendon tears can help clinical decision-making. • Deep learning enables the detailed classification of supraspinatus tendon injuries. • The proposed automated multitask deep learning system is comparable to radiologists.

Coronary artery calcification score and 19 biomarkers on cardiovascular events; a 10-year follow-up DanRisk substudy

Abstract Background SCORE2 is recommended for the estimation of the 10-year risk of cardiovascular disease (CVD), and guideance of medical therapy. However, the prediction is deficient. Coronary artery calcification (CAC) score has been proven to improve the risk assessment in primary prevention of CVD, however, it is also resource-intensive. Biomarkers may be an advantageous alternative. Purpose This study aims to determine and compare the additive value of CAC-score and 19 biomarkers in risk prediction. Methods Classic cardiovascular (CV) risk factors, CAC-score, and a wide range of blood samples (including lipids, calcium-phosphate metabolism, troponin, inflammation, and kidney function), were collected from 1,211 randomly selected middle-aged men and women in this multicentre prospective cohort in 2009-2010. 10-year follow-up data on CV-events (death from CVD, stroke, myocardial infarction, heart failure or coronary artery revascularization) were obtained from the Danish Health Registries. The association between SCORE2, CAC-score, biomarkers, and CV-events was displayed in Kaplan Meier diagrams, assessed using cox proportional hazard rates (HR) and compared using area under the curve (AUC) calculation of receiver operating characteristic (ROC) curves. The net reclassification improvement (NRI) for CAC-score compared to SCORE2 was also calculated. Results 92 participants had CV-events. Events were seen in 29.3% of 41 participants with a very high risk (SCORE2 ≥10%) and 24.2% of 66 participants with CAC-score ≥400. Adjusted for risk factors, CAC-score was significantly associated with events adjusted HR 1.91 (95%CI: 1.10;3.31), 3.61 (95%CI: 1.93;6.76), and 5.2 (95%CI: 2.63;10.29) for CAC-score 1-99, CAC-score 100-399 and CAC-score ≥400, respectively. HR for the highest quartile of C-reactive protein (CRP) was 2.34 (95%CI: 1.15;4.47), while none of the remaining biomarkers improved HR. CAC-score improved AUC of the SCORE2 model (AUC_CAC+SCORE2: 0.72, AUC_SCORE2: 0.67, p&amp;lt;0.01) as seen in Figure 1. A combination of selected biomarkers (total cholesterol, low-density lipoprotein, phosphate, troponin I, CRP, and creatinine) borderline improved AUC (AUC_Biomarkers+SCORE2: 0.71, AUC_SCORE2: 0.67, p=0.06). Inclusion of CAC-score in the SCORE2 model improved the risk prediction. For participants with a CV-event, 73.9% had increased probabilities and 26.1% had decreased probabilities, compared to 42.4% increased and 57.6% decreased probabilities for participants without an event. NRI was 63% (p&amp;lt;0.0001) Conclusion CAC-score remained to improve the prediction of CV-events, while the selected biomarkers did not.ROC of SCORE2 versus CAC-score+SCORE2

Anisotropic Interlayer Force Field for Group-VI Transition Metal Dichalcogenides.

An anisotropic interlayer force field that describes the interlayer interactions in homogeneous and heterogeneous interfaces of group-VI transition metal dichalcogenides (MX2, where M = Mo, W, and X = S, Se) is presented. The force field is benchmarked against density functional theory calculations for bilayer systems within the Heyd-Scuseria-Ernzerhof hybrid density functional approximation, augmented by a nonlocal many-body dispersion treatment of long-range correlation. The parametrization yields good agreement with the reference calculations of binding energy curves and sliding potential energy surfaces. It is found to be transferable to transition metal dichalcogenide (TMD) junctions outside of the training set that contain the same atom types. Calculated bulk moduli agree with most previous dispersion-corrected density functional theory predictions, which underestimate the available experimental values. Calculated phonon spectra of the various junctions under consideration demonstrate the importance of appropriately treating the anisotropic nature of the layered interfaces. Considering our previous parametrization for MoS2, the anisotropic interlayer potential enables accurate and efficient large-scale simulations of the dynamical, tribological, and thermal transport properties of a large set of homogeneous and heterogeneous TMD interfaces.

Diagnostic value of peripheral blood eosinophils for benign and malignant pulmonary nodule.

This retrospective study aims to assess the diagnostic utility of peripheral blood eosinophil counts in distinguishing between benign and malignant pulmonary nodules (PNs) prior to surgical intervention. We involved patients presenting with PNs measuring ≤30 mm as the primary CT imaging finding prior to surgical procedures at the General Hospital of Northern Theater Command in Shenyang, China, during the period spanning 2021 to 2022. Multivariable logistic regression analysis and receiver operator characteristic curve analysis, along with area under the curve (AUC) calculations, were used to determine the diagnostic value of eosinophil. A total of 361 patients with PN were included, consisting of 135 with benign PN and 226 with malignant PN. Multivariable logistic regression analysis showed that eosinophil percentage (OR = 1.909, 95% CI: 1.323-2.844, P < .001), absolute eosinophil value (OR = 0.001, 95% CI: 0.000-0.452, P = .033), tumor diameter (OR = 0.918, 95% CI: 0.877-0.959, P < .001), nodule type (OR = 0.227, 95% CI: 0.125-0.400, P < .001), sex (OR = 2.577, 95% CI: 1.554-4.329, P < .001), and age (OR = 0.967, 95% CI: 0.945-0.989, P = .004) were independently associated with malignant PN. The diagnostic value of regression model (AUC [95% CI]: 0.775 [0.725-0.825]; sensitivity: 74.3%; specificity: 71.1%) was superior to eosinophil percentage (AUC [95% CI]: 0.616 [0.556-0.677]; specificity: 66.8%; specificity: 51.1%) (Delong test: P < .001). Peripheral blood eosinophil percentage might be useful for early malignant PN diagnosis, and combining that with other characteristics might improve the diagnostic performance.